# Parallelism

Parallelism is a computing technique in which multiple processors or cores execute code or work simultaneously.

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## Parallelism and concurrency in a distributed event loop

DevFeed: [Parallelism and concurrency in a distributed event loop](<https://devfeed.tech/articles/parallelism-and-concurrency-in-a-distributed-event-loop-35936.md>)

Original publisher: [Read original article](<https://temporal.io/blog/parallelism-and-concurrency-in-a-distributed-event-loop>)

Author: Fitz

Published: 2023-08-15T07:00:00Z

Content type: tutorial

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Parallelism](<https://devfeed.tech/topics/parallelism.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [concurrency](<https://devfeed.tech/tags/concurrency.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [parallelism](<https://devfeed.tech/tags/parallelism.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

This explanatory article distinguishes concurrency from parallelism and describes how Temporal provides reliable concurrency and parallelism through a durable, distributed event loop.

### Source excerpt

Explore the differences between concurrency and parallelism, and how Temporal offers reliable distributed systems with built-in concurrency and parallelism.

## Kotlin Coroutines Best Practices

DevFeed: [Kotlin Coroutines Best Practices](<https://devfeed.tech/articles/best-practices-39232.md>)

Original publisher: [Read original article](<https://kt.academy/article/cc-best-practices>)

Published: 2023-04-24T00:00:00Z

Content type: article

Language: en

Sources: [Kt. Academy](<https://devfeed.tech/sources/kt-academy.md>)

Topics: [Coroutines](<https://devfeed.tech/topics/coroutines.md>), [Kotlin](<https://devfeed.tech/topics/kotlin.md>), [kotlin-coroutines](<https://devfeed.tech/topics/kotlin-coroutines.md>), [async](<https://devfeed.tech/topics/async.md>), [await](<https://devfeed.tech/topics/await.md>), [Android](<https://devfeed.tech/topics/android.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Parallelism](<https://devfeed.tech/topics/parallelism.md>), [Unit testing](<https://devfeed.tech/topics/unit-testing.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [async](<https://devfeed.tech/tags/async.md>), [await](<https://devfeed.tech/tags/await.md>), [backend](<https://devfeed.tech/tags/backend.md>), [coroutines](<https://devfeed.tech/tags/coroutines.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [kotlin-coroutines](<https://devfeed.tech/tags/kotlin-coroutines.md>), [parallelism](<https://devfeed.tech/tags/parallelism.md>), [unit-testing](<https://devfeed.tech/tags/unit-testing.md>), [workshop-learning-programming](<https://devfeed.tech/tags/workshop-learning-programming.md>)

### AI overview

A concise review of Kotlin coroutine practices, including avoiding unnecessary async/await usage, preferring awaitAll in suitable cases, keeping suspending functions safe across threads, selecting appropriate dispatchers, injecting dispatchers for unit testing, and using yield in CPU-intensive or blocking work.

### Source excerpt

Let's review the Kotlin Coroutines best practices.

## From RxJava 2 to Kotlin Flow: Threading

DevFeed: [From RxJava 2 to Kotlin Flow: Threading](<https://devfeed.tech/articles/from-rxjava-2-to-kotlin-flow-threading-38624.md>)

Original publisher: [Read original article](<https://krossovochkin.com/posts/2020_01_25_from_rxjava_2_to_kotlin_flow_threading/>)

Published: 2020-01-25T00:00:00Z

Content type: tutorial

Language: en

Sources: [Vasya Drobushkov](<https://devfeed.tech/sources/vasya-drobushkov.md>)

Topics: [RxJava](<https://devfeed.tech/topics/rxjava.md>), [kotlin-flow](<https://devfeed.tech/topics/kotlin-flow.md>), [Kotlin](<https://devfeed.tech/topics/kotlin.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [reactive](<https://devfeed.tech/topics/reactive.md>), [Parallelism](<https://devfeed.tech/topics/parallelism.md>), [Android](<https://devfeed.tech/topics/android.md>), [Programming](<https://devfeed.tech/topics/programming.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [kotlin-flow](<https://devfeed.tech/tags/kotlin-flow.md>), [observeon](<https://devfeed.tech/tags/observeon.md>), [parallelism](<https://devfeed.tech/tags/parallelism.md>), [reactive](<https://devfeed.tech/tags/reactive.md>), [reactive-programming](<https://devfeed.tech/tags/reactive-programming.md>), [rxjava](<https://devfeed.tech/tags/rxjava.md>), [streams](<https://devfeed.tech/tags/streams.md>), [subscribeon](<https://devfeed.tech/tags/subscribeon.md>)

### AI overview

This article compares threading in RxJava 2 and Kotlin Flow. It explains cold streams and the roles of subscribeOn and observeOn, including how schedulers determine where a reactive chain starts and continues.

### Source excerpt

Source Introduction For a long time RxJava was undisputed leader for reactive solutions on Android, though with Kotlin expansion and introducing cold streams (Flow) seems situation might be rapidly changing in the coming years. Though reactive programming is not related to threading in the first place, concurrency and parallelism are very important anyway. In this article we'll try to make short recap on threading in RxJava 2 (with some basic caveats on its usage) and then take a look at how threading works in Kotlin Flow, so if anyone would like to migrate their code without affecting functionality it would be nice and smooth.